Research Papers

Exponential Synchronization of Stochastic Reaction-Diffusion Fuzzy Cohen-Grossberg Neural Networks With Time-Varying Delays Via Periodically Intermittent Control

[+] Author and Article Information
Qintao Gan

Department of Basic Science,
Shijiazhuang Mechanical Engineering College,
97 Heping West Road,
Shijiazhuang, Hebei Province 050003, China
e-mail: ganqintao@sina.com

Yang Li

Department of Vehicle and Electrical Engineering,
Shijiazhuang Mechanical Engineering College,
Shijiazhuang, Hebei Province 050003, China

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 28, 2012; final manuscript received July 12, 2013; published online August 23, 2013. Assoc. Editor: Warren E. Dixon.

J. Dyn. Sys., Meas., Control 135(6), 061009 (Aug 23, 2013) (8 pages) Paper No: DS-12-1071; doi: 10.1115/1.4025157 History: Received February 28, 2012; Revised July 12, 2013

In this paper, the exponential synchronization problem for fuzzy Cohen-Grossberg neural networks with time-varying delays, stochastic noise disturbance, and reaction-diffusion effects are investigated. By introducing a novel Lyapunov-Krasovskii functional with the idea of delay partitioning, a periodically intermittent controller is developed to derive sufficient conditions ensuring the addressed neural networks to be exponentially synchronized in terms of p-norm. The results extend and improve upon earlier work. A numerical example is provided to show the effectiveness of the proposed theories.

Copyright © 2013 by ASME
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Grahic Jump Location
Fig. 1

Chaotic behavior of delayed reaction-diffusion fuzzy Cohen-Grossberg neural network model (4.1)

Grahic Jump Location
Fig. 2

Synchronization errors e1(t, x) and e2(t, x) between systems (4.1) and (4.2)




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